4.4.1. Scenario Terminology

In this section representative quantifications of the four scenario storylines described in Section 4.3 are summarized, and the evolution of the main scenario driving forces and associated quantitative scenario characteristics are described. Their resultant GHG and other emissions are discussed in more detail in Chapter 5.

To elucidate differences in uncertainties that stem both from adopting alternative (exogenous) scenario driving-force assumptions and from the uncertainties that arise from different model representations, alternative scenario quantifications are differentiated into harmonized and unharmonized scenarios (see Section 4.2, Tables 4-1 and 4-2, and Box 1-1 for terminology description).

To achieve harmonization across six different modeling approaches is not a trivial task. For example, most of the models have different regional disaggregations, so that harmonization at the level of the four SRES regions required some "inverse" solutions, often achieved through iterative model runs and adjustments of input assumptions. Also, in some modeling frameworks the harmonized "input" parameters are actually outputs of components of the modeling framework (e.g., GDP as an output of economic general equilibrium models, or final energy as an output variable after considering endogenous energy prices and exogenously pre-specified energy-intensity improvement rates). Therefore, harmonization of important scenario driving-force inputs was neither possible for all scenarios and for all participating modeling teams, and nor was it judged desirable, as the adoption of any harmonization criterion somewhat artificially compresses uncertainty. This is also why simpler harmonization criteria were adopted (see Section 4.2. above) that focused on global population and GDP growth profiles. These are referred to as "globally harmonized" scenarios in the subsequent Subsections.

From the 40 SRES scenarios, 26 are classified as "globally harmonized" scenarios and 14 are classified as "other" scenarios. (The latter category includes three scenarios that only deviate slightly from the harmonization criteria.) Harmonized scenarios are thus comparable in that they describe similar global development patterns with respect to demographics and economic growth. In the subsequent discussion of scenario driving forces a three-tiered structure is adopted. First, for each scenario family (and where applicable for each scenario group in the A1 scenario family), the discussion starts with a presentation of the respective marker and "fully harmonized" scenarios. Subsequently, "globally harmonized" scenarios and "other" scenarios are discussed. "Globally harmonized" scenarios shed additional light into uncertainties that stem from adopting different regional assumptions (see above). Finally, "other" scenarios are presented that offer a different quantitative interpretation of a particular scenario storyline compared to the previous scenario categories. In some cases, differences in interpretation relate to uncertainties in rates of change - "other" scenarios yield similar global demographic and economic outcomes by 2100 (e.g. the B2-ASF scenario compared to the B2 marker), but illustrate different dynamics of how these could unfold. In other cases, the "other" scenario category comprises scenario quantifications that deliberately explore alternative interpretations of a scenario storyline in terms of global population and GDP growth altogether (e.g. in the A2-A1- MiniCAM scenario). The reason is to indicate that quantitative scenario descriptions entail a high degree of uncertainty (and subjectivity from different modeling teams) when it comes to interpret the four different qualitative SRES scenario storylines and to translate them into the quantitative assumptions that drive emission models. When comparing GHG emissions results for the four SRES marker scenarios (see Chapter 5) with those of the other SRES scenarios, it is illustrative to distinguish the effects of different model methodologies and parametrizations from variations of important scenario drivers that often serve as exogenous input to models.

Of the total of 40 SRES scenarios, 29 (including the marker scenarios) satisfy the harmonization criteria for population on the world level and for all four SRES regions, 12 scenarios are harmonized for population and GDP, and 11 (13 including the A1T scenario group) scenarios are harmonized for population, GDP and final energy (see Table 4-1). Also, 35 scenarios are harmonized for population on the world level and 26 scenarios are harmonized for global population and GDP (see Table 4-1). The status of harmonization is also relatively stable to changes in the harmonization criteria. For example, if the above harmonization criteria were increased by 50% (i.e. GDP for the four SRES regions may differ by up to ±38% from the respective GDP of the marker scenario), the sample of 11 harmonized scenarios does not change; however, the number of scenarios harmonized on the global level increases from 15 to 20.

Thus, as mentioned above not all scenario quantifications comply with the adopted harmonization criteria differences in regional coverage and definition among models. In some instances modeling teams also deliberately chose not to follow harmonized input assumptions, but instead explored scenario sensitivities by emphasizing alternative developments than suggested in the marker scenario quantification. The writing team recognizes that this increases the number of scenarios as well as complexity in the interpretation of results. These additional scenarios are the result of the SRES terms of reference of proceeding via an open process soliciting as wide participation and viewpoints as possible and also serve the purpose of highlighting important uncertainties of the future that are necessarily compressed by limiting scenario quantification to four illustrative marker scenarios. Thus, while unharmonized scenarios illustrate the impact on GHG emissions of expanding the uncertainty range of main scenario drivers within any particular scenario family, the "globally harmonized" scenarios indicate the range of GHG emissions uncertainty that remains after most important global driving force assumptions (population and GDP) have been harmonized. (Finally, the range of GHG emissions resulting from comparing "fully harmonized" scenarios is indicative of the uncertainty of internal model parametrizations such as energy technology change, dietary patterns, and agricultural productivity changes that influence structural changes in energy supply and end-use and land-use changes, see Table 4-1.)

Harmonization of input assumptions increases the comparability across scenarios and can serve as an additional guide for choosing a particular SRES scenario subset, and to illustrate different degrees of scenario uncertainty. The latter is an important aspect, considering the different user communities of SRES scenarios. Given the comparatively narrow variation as defined by the harmonization criteria, differences in population, GDP, and final energy use between harmonized scenarios of the same scenario family need not to be considered in subsequent analyses and are also not discussed separately below.

In the A1 scenario family, the scenarios within one group were also harmonized. In one A1 scenario group the transition away from conventional oil and gas either leads to a massive development of unconventional oil and gas resources (A1G) or to a large-scale synfuel economy based on coal (A1C). Please note that A1C and A1G were combined into one fossil intensive group A1FI in the Summary for Policymakers during its approval process (see also footnote 1). GHG emissions in these scenarios approach emissions characteristic of the A2 scenario family (i.e. are much higher than in the case of the A1 marker scenario). In another A1 scenario group, dwindling conventional oil and gas resources lead to fast development of post-fossil alternatives and enhanced energy conservation. In this technology-intensive scenario group (A1T), energy demands are lower than in the other A1 scenario groups and, because of radical technological change in energy systems, GHG emissions are much lower than in the other A1 scenario groups (including the A1B marker scenario), approaching those of the B1 scenario family.

The six modeling teams also produced other scenarios as part of the SRES open process. These modeling runs were generally not harmonized and are presented as appropriate later in the report.

Table 4-3 gives an overview of the 40 SRES scenario quantifications as they were developed to describe the four scenario families and the seven different scenario groups.

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